• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.2
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• pp.151-157
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• 1996

This study, experimentally, aims at presenting the methodology to construct an efficient digital terrain by com-paring and analyzing the accuracy among the existing Digital Terrain Models, develope 3-D fractal terrain model-ling program by applying digital algorithm of fractal geometry and using turbo pascal, and lastly perform basic research on constructing GSIS-based 3-D fractal terrain modelling system by integrating a PC-based GSIS Pack-age and the 3-D fractal terrain modelling program developed by this paper. The results are as follows -First, the method to produce TIN(Triangulated Irregular Network) by the combination of point data and line data was showed as an alternative to construct efficient Digital Terrain Model. Second, developing GSIS-based 3-D fractal terrain modelling system, applying fractal geometry is the basic research in developing the new terrain modelling method. also, this study presented the possibility of 3-D terrain modelling with the use of fractal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Journal of the Korean housing association
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• v.24 no.6
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• pp.123-132
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• 2013

This study covers centrality of villas after Renaissance and architectural forms corresponding to the nature in the aspect of Karl Popper and Gilles Deleuze. In addition, the researcher tries to understand the changes in architectural forms according to the changes in places. To do that, Pascal and Hans Meyer's theory was introduced as a major theory. The researcher tries to set up the standard by analyzing the architectural form related with the nature and the form of center in the housing through Villa Rotonda and Villa Emo. And with key analysis of the centrality and architectural form corresponding to the nature and the four houses that are historically and morphologically important. It is found that due to the development in modern technology and materials, a variety of architectural approach is developed and they are implemented as various architectural forms. However, due to various theories of modern architectural approach, it is found that the morphological differences of suburban housing exists by forming a universal relationship constructed by four architects. The researcher expects to be understood and realized in real life construction.

• Structural Engineering and Mechanics
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• v.61 no.3
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• pp.325-334
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• 2017

In the present study, a numerical model for probability analysis of optimal design of fatigue non-uniform crack growth behaviour of a cracked aluminium 2024 T3 plate repaired with a bonded composite patch is investigated. The proposed 3D numerical model has advanced in literatures, which gathers in a unique study: problems of reliability, optimization, fatigue, cracks and repair of plates subjected to tensile loadings. To achieve this aim, a finite element modelling is carried out to determine the evolution of the stress intensity factor at the crack tip Paris law is used to predict the fatigue life for a give n crack. To have an optimal volume of our patch satisfied the practical fatigue life, a procedure of optimization is proposed. Finally, the probabilistic analysis is performed in order to a show that optimized patch design is influenced by uncertainties related to mechanical and geometrical properties during the manufacturing process.

• Annual Conference on Human and Language Technology
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• 2005.10a
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• pp.7-12
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• 2005

본 논문은 은닉 마코프 모델(hidden Markov Model: HMM)을 2 단계로 적용하여 논문 모집공고(Call-for-Paper: CFP)에서 필요한 정보를 추출하는 방법을 제안한다. HMM은 순차적인 흐름의 정보를 담고 있는 데이터를 잘 설명할 수 있으며 CFP가 담고 있는 정보에는 순서가 있기 때문에, CFP를 HMM으로 설명할 수 있다. 하지만, 문서를 전체적으로(global) 파악하는 HMM만으로는 정보의 정확한 경계를 파악할 수 없다. 따라서 첫 번째 단계로 CFP문서에서 구(phrase) 단위를 구성하는 단어의 열에 대한 HMMs을 통해 국부적으로(local) 정보의 경계와 대강의 종류를 파악한다. 그리고 두 번째 단계에서 전체적인 문서의 내용 흐름에 근거하여 구축된 HMM을 이용하여 그 정보가 세부적으로 어떤 종류의 정보인지 정한다. PASCAL challenge에서 제공받은 Cff 말뭉치에 대한 첫 번째 단계의 실험 결과, 0.60의 재현률과 0.61의 정확률을 보였으며, 정확률과 재현률을 바탕으로 F-measure를 측정한 결과 0.60이었다.

• Journal for History of Mathematics
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• v.20 no.4
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• pp.61-70
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• 2007

Combinatorics, often called the 21 st century mathematics, has turned out a very important subject for the present information era. Modern combinatorics has started from some mathematical works, for example, Pascal's triangle and the binomial coefficients, and Euler's problems on the partitions of integers and Konigsberg's bridge problem, and so on. In this paper, we investigate the origin of combinatorics by looking over some interesting ancient combinatorial problems and some important problems which have started various subfields of combinatorics. We also discuss a little on the role of combinatorics in mathematics and mathematics education.

• Annual Conference of KIPS
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• 2016.10a
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• pp.506-509
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• 2016

We present real-time pedestrian detection that exploit accuracy of Faster R-CNN network. Faster R-CNN has shown to success at PASCAL VOC multi-object detection tasks, and their ability to operate on raw pixel input without the need to design special features is very engaging. Therefore, in this work we apply and adjust Faster R-CNN to single object detection, which is pedestrian detection. The drawback of Faster R-CNN is its failure when object size is small. Previously, small sized object problem was solved by Scale-aware Network. We incorporate Scale-aware Network to Faster R-CNN. This made our method Scale-aware Faster R-CNN (DF R-CNN) that is both fast and very accurate. We separated Faster R-CNN networks into two sub-network, that is one for large-size objects and another one for small-size objects. The resulting approach achieves a 28.3% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the other best reported results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3180-3199
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• 2013

While non-predefined object segmentation (NDOS) distinguishes an arbitrary self-assumed object from its background, predefined object segmentation (DOS) pre-specifies the target object. In this paper, a new and novel method to segment predefined objects is presented, by globally optimizing an orientation-based objective function that measures the fitness of the object boundary, in a discretized parameter space. A specific object is explicitly described by normalized discrete sets of boundary points and corresponding normal vectors with respect to its plane shape. The orientation factor provides robust distinctness for target objects. By considering the order of transformation elements, and their dependency on the derived over-segmentation outcome, the domain of translations and scales is efficiently discretized. A branch and bound algorithm is used to determine the transformation parameters of a shape model corresponding to a target object in an image. The results tested on the PASCAL dataset show a considerable achievement in solving complex backgrounds and unclear boundary images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4080-4097
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• 2020

In recent years, convolutional neural network (CNN) has become the primary method for face detection. But its shortcomings are obvious, such as expensive calculation, heavy model, etc. This makes CNN difficult to use on the mobile devices which have limited computing and storage capabilities. Therefore, the design of lightweight CNN for face detection is becoming more and more important with the popularity of smartphones and mobile Internet. Based on the CPU real-time face detector FaceBoxes, we propose a multi-task lightweight face detector, which has low computing cost and higher detection precision. First, to improve the detection capability, the squeeze and excitation modules are used to extract attention between channels. Then, the textual and semantic information are extracted by shallow networks and deep networks respectively to get rich features. Finally, the landmark detection module is used to improve the detection performance for small faces and provide landmark data for face alignment. Experiments on AFW, FDDB, PASCAL, and WIDER FACE datasets show that our algorithm has achieved significant improvement in the mean average precision. Especially, on the WIDER FACE hard validation set, our algorithm outperforms the mean average precision of FaceBoxes by 7.2%. For VGA-resolution images, the running speed of our algorithm can reach 23FPS on a CPU device.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6009-6027
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• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.